3 Benefits of a Digital Twin and How it is Used in Industry

3 Benefits of a Digital Twin and How it is Used in Industry

The idea of a digital twin has been around for many years, but only recently has become widely accessible. With the increasing emergence of digital transformation and Industry 4.0, data collection and data analysis have progressed incredibly, paving the way for digital twin technology to become a cost effective, and powerful tool for any industrial organization.

I’m sure many are wondering “what is a digital twin?” Simply put, it is a digital copy of a physical object, system, or process. To further illustrate, one of the earliest digital twins was used during the Apollo 13 space mission. When one of the spacecraft’s oxygen tanks malfunctioned, the team on Earth was able to quickly modify their training simulators to replicate the real world scenario that the astronauts were facing 330,000 km from Earth — essentially creating the first digital twin. By creating a digital replica, they were able to diagnose the problems occurring on the craft and propose solutions that ultimately lead to safe return.

In recent years, digital twins have become much more accessible to companies all over the world. Manufacturing seems to be the first sector to adopt the technology, but it is now proving its value across more and more industrial sectors.

In this blog I have laid out the main uses of a digital twin and examples of how it can be used:

Predictive Insights

One of the most powerful uses of a digital twin is for predictive maintenance. Physical assets and equipment can be digitally replicated by extracting various forms of data, such as mechanical properties, usage, operating conditions, etc… With an accurate digital replica of the physical equipment, simulations can then be performed in rapid succession in order to predict when the equipment will fail. Being able to see the future life of the equipment prevents unplanned downtime and keeps operations running smoothly. 

A few predictive insight use cases from various industries:

  • Agriculture: predict maintenance on tractor, front end loader, cultivator, or any type of machinery before it is required
  • Energy: predict storage tank leakage or pump jack failure ahead of time to plan a maintenance schedule
  • Manufacturing: predict equipment failure on drilling, honing, packing and other machines to prevent unplanned downtime
  • Mining: predict maintenance needed on dragline or conveyor machines and optimize maintenance schedules to best keep operations flowing

Remote Diagnostics

Not only can you predict equipment failure ahead of time, but you can also use a digital twin to diagnose issues with equipment from remote locations. As illustrated in the Apollo 13 example above, having a virtual replica allows you to analyze areas of concern from anywhere and ultimately diagnose what the issue is. This is a valuable resource especially when your operations are in remote or unsafe areas. 

A few remote diagnostic use cases from various industries:

  • Agriculture: diagnose specific areas of crops that have contracted disease or infestations across thousands of acres of land all from a single computer
  • Energy: diagnose the issue and location of a leaking pipeline for easier maintenance scheduling
  • Manufacturing: diagnose products that have been manufactured and deployed in the field without having to send a maintenance team to inspect the issues
  • Mining: diagnose and propose solutions to broken mining equipment without having to go through costly process of physical inspection at multiple remote located sites

Process Optimization

A digital twin can also model a process rather than a physical piece of equipment. By deploying data collection sensors throughout a production line or other process, you can create a digital twin of the entire operation. Similar to the predictive insights, sensors can be attached to all equipment in a process and simulations are run to identify opportunities to increase efficiency. 

A few process optimization use cases from various industries:

  • Agriculture: sensors to monitor soil conditions and weather data can be used to simulate the growth process and suggest ways to optimize the amount of water and fertilizer needed for less waste
  • Energy: applying a digital twin to the entire oil & gas supply chain helps to identify bottlenecks and create the most efficient route possible from the ground to consumer
  • Manufacturing: sensors across the entire manufacturing line can accurately simulate the production process, finding bottlenecks and suggesting ways to improve efficiency
  • Mining: a digital twin of the mining site and vehicles allows operators to run simulations and map out the optimal route for mining vehicles to take for maximum efficiency

Conclusion

A digital twin is an excellent way to increase efficiencies and bring value to any organization. Whether it be agriculture, energy, manufacturing, mining or any industrial sector, digital twins will take a company’s digital strategy to the next level through predictive insights, remote diagnostics, and process optimization. 

If you are ready to start your digital twin project today, get a hold of one of our experts at sales@A4.Systems and we will be glad to discuss your project and guide you on the right path.